You open your favorite online shopping app, and there it is: a curated selection of products that perfectly align with your style and preferences. It’s almost like your digital genie, knowing exactly what you’re looking for before you do. This isn’t by mere coincidence or luck; it’s the brilliance of product recommendation engines at work. These clever systems work by sifting through data, and bringing you those perfect picks. And, they’re the reason your online shopping feels less like searching and more like discovering.
What is a Recommendation Engine?
At its core, a product recommendation engine is a sophisticated system that suggests products to users based on a number of criteria, inclusive of but not limited to: browsing history, purchase history, and user preferences. From the early days of basic algorithms to today’s AI-driven systems, these engines have evolved to become an integral part of the e-commerce experience.
This guide will explore the workings of these engines, the different types of engines that companies use, and their impact on e-commerce business, while providing insights into how they can revolutionize your online store.
Applications in E-Commerce
Upselling and Cross-Selling with Personalized Recommendations
These engines excel at suggesting complementary or higher-end products for consumers. For example, Apple leverages recommendation systems to suggest accessories and upgrades, significantly increasing average order value.
Apple Case Study: “The Price Ladder”
Let’s say you want to purchase a tablet from Apple. Apple may appeal to the cost-conscious consumer by getting them “in the door” with the $329.00 cheapest iPad. However, a salesperson or Apple’s website will often show upgrades that are marginal in cost compared to the baseline iPad, with the strategy of eventually walking you up the price ladder. For example, the next cheapest iPad is $449.00, and so on.
Content and New Category Recommendations
These engines can also recommend products or content to its users that align with their interests. Recommendations are made based on a users’ past history, therefore all recommendations are bespoke to each user. This personalized approach is a big deal – Epsilon Marketing tells us that a whopping 80% of consumers are more likely to make purchases from a company that provides tailored recommendations.
Take Amazon, for example. They make product recommendations by suggesting products that other customers, who have bought similar items, also purchased. For instance, customers who buy a specific VR headset may also be recommended a compatible travel case.
Aiding Product Discovery
In an e-commerce landscape with endless choices, guiding customers to discover products they love is crucial. Company Folders, for instance, achieved a 68% increase in total quotes by simplifying its product discovery process for consumers, by distilling 15 million product combinations into a single-page form.
Types of Product Recommendation Engines and Utility Based on Data Availability
Collaborative Filtering in User-Data-Rich Environments
Collaborative filtering relies on user behavior such as likes, purchases, and views to make product recommendations. “Users who bought this also bought…” is a familiar phrase resulting from collaborative filtering. A classic example is Amazon, where this strategy drives 35% of all of its sales. Collaborative filtering thrives in scenarios where there's a wealth of user interaction data, such as ratings, purchases, or viewing histories.
This filtering has the added advantage of uncovering hidden consumer preferences, and typically works best for well-established platforms rich in user data. A drawback with using collaborative filtering is that it struggles with pushing new items to consumers (see the Cold Start problem below), and it is a strategy that typically will not work if you do not have large amounts of user data.
The Cold Start Problem in User-Data-Poor Environments
To put it simply: the Cold Start Problem is like trying to suggest a new coffee shop to a friend when you barely know their coffee preferences. This issue pops up a lot when these systems encounter new users who haven't interacted much, or when there are brand new items that don't have a lot of user data yet.
Spotify Case Study: How do new songs get recommended?
Spotify often encounters the Cold Start Problem, particularly when new songs may fail to get recommended simply because of a lack of listening data. Spotify uses a clever workaround; by processing the raw audio, analyzing audio features, and using natural language processing to analyze textual or lyrical data, the new song is able to be placed into the music ecosystem and get recommended to listeners.
Content-Based Filtering in Product-Data-Rich Environments
Content-based recommendation engines, epitomized by platforms like Netflix, operate on a straightforward yet effective principle: recommending products based on their inherent features such as tags, categories, and descriptions. This method excels in suggesting new items, as it relies on specific attributes of each product, allowing for a more controlled recommendation process.
One of the notable strengths of content-based filtering is its ability to overcome challenges in early-stage customer data accumulation. It proves especially beneficial for introducing new items or services, effectively addressing the Cold Start Problem that can often hinder collaborative filtering algorithms. This method ensures that newer products are adequately exposed and recommended, fostering early engagement and discovery by customers.
However, the efficacy of this method is bound by the limitations of known item features and may not fully encompass a user's preferences that extend beyond these characteristics. Netflix exemplifies this approach's success, expertly curating personalized content recommendations for its users, thereby enhancing their viewing experience by aligning suggestions closely with individual tastes and preferences.
Hybrid Filtering Systems
Hybrid recommendation systems cleverly combine the strengths of both collaborative and content-based filtering. They mix information from users' actions (like what they buy or watch) with detailed aspects of the items themselves, such as music genres or artist details. This combination helps overcome the drawbacks of using either method alone, resulting in a more versatile and accurate recommendation process.
However, setting up such a system can be complex, as it involves integrating two different types of data. Spotify's "Discover Weekly" Playlist is a prime example of this approach, where it blends data about users' listening habits with musical characteristics to create personalized playlists.
The adaptability of hybrid filtering systems makes it a robust choice for businesses seeking to enhance the personalization of their customer experience. It capably addresses the limitations of solely relying on one type of data, providing a more rounded and accurate recommendation system.
Putting Recommendation Engines to Use
E-Commerce Websites
This is perhaps the most common use case. Recommendation engines on e-commerce platforms can suggest products to customers based on their browsing and purchase history, increasing the likelihood of additional purchases.
Email Marketing Campaigns
By integrating recommendation engines with email marketing tools, businesses can send personalized product suggestions or content to customers, improving the effectiveness of their marketing efforts.
Implementation of Recommendation Engines
Building an In-House Product Recommendation System
An in-house product recommendation system requires technical expertise, and is often time-consuming. Additionally, ongoing maintenance and updates are necessary to keep the recommendation system accurate and relevant as customer behavior and preferences change. A standard process for building these engines is as follows:
- Data Consolidation: The first step is to consolidate all relevant data into a data warehouse. This includes tracking data, purchase history, and product data. Ensure that the data is clean, well-structured, and regularly updated.
- Model Development: Building a recommendation model that leverages the consolidated data is a crucial part of the process. It requires data scientists or machine learning experts. Oftentimes, testing of different models is necessary depending on the specifics of your data.
- Integration with Communication Platforms: After successfully building and testing the recommendation model, the next step is to integrate it into your communication platforms, such as email marketing tools, website, or mobile apps, for practical use in marketing campaigns.
Klaviyo
If you are using Shopify and Klaviyo, you have an alternative option. Klaviyo offers a recommendation engine that can be easily integrated into Klaviyo email campaigns. While this solution is convenient and requires less technical expertise, it has limitations. For example, Klaviyo's recommendations are not highly customizable. You may have limited control over how recommendations are presented and personalized.
Cotera - A Flexible Solution
Cotera offers the best of both worlds. It provides a cost-effective and efficient solution that doesn't require building everything from scratch. Here's why Cotera is valuable:
- Cost-Effective: Cotera is more affordable and quicker to implement compared to an in-house solution, saving you time and resources.
- Customizable: Cotera allows for a high degree of customization, giving you control over how recommendations are presented and tailored to your specific needs.
- Compatibility: It works seamlessly with various e-commerce stacks, making it versatile and suitable for different platforms.
Takeaways
In the bustling world of e-commerce, product recommendation engines are now an integral part of the shopping experience. These ingenious systems sift through user preferences and browsing histories to curate a selection of products tailored just for you. It's like having a virtual assistant who knows exactly what you're looking for, even before you do.
Enter Cotera–a game-changer in this arena.
What sets Cotera apart is its blend of cost-efficacy, customizability, and wide-ranging compatibility. Unlike the route of developing an in-house engine, which can be both costly and time-consuming, or using solutions like Klaviyo, which might offer less flexibility, Cotera strikes the perfect balance. It's designed to seamlessly fit into various online retail environments, making it an incredibly adaptable choice for businesses big and small. This adaptability and affordability make Cotera not just a tool but a strategic asset in the highly competitive e-commerce landscape.